@inproceedings{10.1145/3308532.3329462,
author = {Malik, Usman and Barange, Mukesh and Ghannad, Naser and Saunier, Julien and Pauchet, Alexandre},
title = {A Generic Machine Learning Based Approach for Addressee Detection In Multiparty Interaction},
year = {2019},
isbn = {9781450366724},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3308532.3329462},
doi = {10.1145/3308532.3329462},
abstract = {Addressee detection is one of the most fundamental tasks for seamless dialogue management and turn taking in human-agent interaction. Whereas addressee detection is implicit in dyadic interaction, it becomes a challenging task in multiparty interactions when more than two participants are involved. Existing research works employ either rule-based or statistical approaches for addressee detection. However, most of these works either have been tested on a single data set or only support a fixed number of participants. In this article, we propose a model based on generic features to predict the addressee in data sets with varying number of participants. The results tested on two different corpora show that the proposed model outperforms existing baselines.},
booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents},
pages = {119–126},
numpages = {8},
keywords = {human-agent interaction, multimodal interaction, mixed communities, machine learning, multiparty inter-action},
location = {Paris, France},
series = {IVA '19}
}